{"id":"W3176755515","doi":"10.48550/arxiv.2101.01975","title":"Predicting Forest Fire Using Remote Sensing Data And Machine Learning","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Deforestation (computer science); Baseline (sea); Computer science; Receiver operating characteristic; Machine learning; Remote sensing; Random forest; Climate change; Artificial intelligence; Environmental science; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008342233,0.0007560786,0.0004045644,0.001515219,0.0002269834,0.0006111821,0.000432978,0.0005654966,0.0005924016],"category_scores_gemma":[0.00254525,0.0001811769,0.0004505021,0.0008090151,0.0001943402,0.0007682721,0.000270757,0.0006115064,0.0003335286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004755702,"about_ca_system_score_gemma":0.0003629541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01517245,"about_ca_topic_score_gemma":0.01467452,"domain_scores_codex":[0.999762,0.0000630733,0.00002015195,0.00006721807,0.00005922105,0.00002831729],"domain_scores_gemma":[0.9990896,0.0005405081,0.0001074265,0.00007272766,0.0001536512,0.00003612876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002318145,0.0006762715,0.09916186,0.00008780517,0.000161246,0.0002097052,0.00004358044,0.6029326,0.004179643,0.0005676911,0.002173505,0.2895743],"study_design_scores_gemma":[0.000003650474,0.00002449994,0.01031379,0.000007144065,0.00001051576,0.00002181731,0.00001872124,0.9878421,0.001024601,0.0005282846,0.0001984687,0.000006369225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.906036,0.001182339,0.0854674,0.0006973871,0.0001378292,0.00009093235,0.00115079,0.001433159,0.003804018],"genre_scores_gemma":[0.9734635,0.0002226185,0.02475935,0.00004359605,0.00005579977,0.00002064133,0.0008692004,0.00001176434,0.0005535712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01517245,"threshold_uncertainty_score":0.03016829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05684153616976605,"score_gpt":0.1777287328481875,"score_spread":0.1208871966784214,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}